




"""simple docstring"""

import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   List[str]				)       ->   str:






      '''simple docstring'''


      _A     =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
      _A     =  Image.open(requests.get(_snake_case							,    stream=_snake_case				).raw				).convert('RGB'				)

      _A     =  transforms.Compose(
          [
              transforms.Resize((image_size, image_size)							,    interpolation=InterpolationMode.BICUBIC				),
              transforms.ToTensor(),
              transforms.Normalize((0.48145466, 0.4578275, 0.40821073)							,    (0.26862954, 0.26130258, 0.27577711)				),
          ]				)
      _A     =  transform(_snake_case				).unsqueeze(0				).to(_snake_case				)
      return image






def 		_snake_case	(   _snake_case				:   List[str]				)       ->   Dict:






      '''simple docstring'''


      if "visual_encoder" in key:
            _A     =  re.sub('visual_encoder*'							,    'vision_model.encoder'							,    _snake_case				)
      if "blocks" in key:
            _A     =  re.sub(R'blocks'							,    'layers'							,    _snake_case				)
      if "attn" in key:
            _A     =  re.sub(R'attn'							,    'self_attn'							,    _snake_case				)
      if "norm1" in key:
            _A     =  re.sub(R'norm1'							,    'layer_norm1'							,    _snake_case				)
      if "norm2" in key:
            _A     =  re.sub(R'norm2'							,    'layer_norm2'							,    _snake_case				)
      if "encoder.norm" in key:
            _A     =  re.sub(R'encoder.norm'							,    'post_layernorm'							,    _snake_case				)
      if "encoder.patch_embed.proj" in key:
            _A     =  re.sub(R'encoder.patch_embed.proj'							,    'embeddings.patch_embedding'							,    _snake_case				)

      if "encoder.pos_embed" in key:
            _A     =  re.sub(R'encoder.pos_embed'							,    'embeddings.position_embedding'							,    _snake_case				)
      if "encoder.cls_token" in key:
            _A     =  re.sub(R'encoder.cls_token'							,    'embeddings.class_embedding'							,    _snake_case				)

      if "self_attn" in key:
            _A     =  re.sub(R'self_attn.proj'							,    'self_attn.projection'							,    _snake_case				)

      return key






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   List[str]=None				)       ->   Any:






      '''simple docstring'''


      if config_path is not None:
            _A     =  BlipConfig.from_pretrained(_snake_case				)
      else:
            _A     =  BlipConfig(projection_dim=5_12							,    text_config={}							,    vision_config={}				)

      _A     =  BlipForConditionalGeneration(_snake_case				).eval()

      _A     =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

      _A     =  blip_decoder(pretrained=_snake_case							,    image_size=3_84							,    vit='base'				)
      _A     =  pt_model.eval()

      _A     =  pt_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      hf_model.load_state_dict(_snake_case				)

      _A     =  3_84
      _A     =  load_demo_image(image_size=_snake_case							,    device='cpu'				)
      _A     =  BertTokenizer.from_pretrained('bert-base-uncased'				)
      _A     =  tokenizer(['a picture of']				).input_ids

      _A     =  hf_model.generate(_snake_case							,    _snake_case				)

      assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

      _A     =  hf_model.generate(_snake_case				)

      assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

      if pytorch_dump_folder_path is not None:
            hf_model.save_pretrained(_snake_case				)

      # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
      _A     =  (
          'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
      )

      _A     =  blip_vqa(pretrained=_snake_case							,    image_size=_snake_case							,    vit='base'				)
      vqa_model.eval()

      _A     =  vqa_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      _A     =  BlipForQuestionAnswering(_snake_case				)

      hf_vqa_model.load_state_dict(_snake_case				)

      _A     =  ['How many dogs are in this image?']
      _A     =  tokenizer(_snake_case							,    return_tensors='pt'				).input_ids

      _A     =  hf_vqa_model.generate(_snake_case							,    _snake_case				)
      print(tokenizer.decode(answer[0]				)				)

      assert tokenizer.decode(answer[0]				) == "[UNK] 1 [SEP]"
      if pytorch_dump_folder_path is not None:
            hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'				)

      _A     =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

      _A     =  blip_itm(pretrained=_snake_case							,    image_size=_snake_case							,    vit='base'				)
      itm_model.eval()

      _A     =  itm_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      _A     =  BlipForImageTextRetrieval(_snake_case				)

      _A     =  ['A picture of a woman with a dog sitting in a beach']
      _A     =  tokenizer(
          _snake_case							,    return_tensors='pt'							,    padding='max_length'							,    truncation=_snake_case							,    max_length=35							,    ).input_ids

      hf_itm_model.load_state_dict(_snake_case				)
      hf_itm_model.eval()

      _A     =  hf_itm_model(_snake_case							,    _snake_case							,    use_itm_head=_snake_case				)
      _A     =  hf_itm_model(_snake_case							,    _snake_case							,    use_itm_head=_snake_case				)

      assert out[0].item() == 0.2110687494277954
      assert torch.nn.functional.softmax(out_itm[0]							,    dim=1				)[:, 1].item() == 0.45698845386505127

      if pytorch_dump_folder_path is not None:
            hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'				)


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       a							      =     parser.parse_args()

       convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''







import argparse
import copy







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {}

   with open(UpperCAmelCase						) as f:
      for line in f:
         if line.split()[0] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[1], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[0]].append(
                [line.split()[1], line.split()[2]]						)
         if line.split()[1] not in dict_of_neighbours:
            A__       =     []
            _list.append([line.split()[0], line.split()[2]]						)
            A__       =     _list
         else:
            dict_of_neighbours[line.split()[1]].append(
                [line.split()[0], line.split()[2]]						)

   return dict_of_neighbours







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      A__       =     f.read(1						)
   A__       =     start_node

   A__       =     []

   A__       =     start_node

   A__       =     0
   while visiting not in first_solution:
      A__       =     10000
      for k in dict_of_neighbours[visiting]:
         if int(k[1]						) < int(UpperCAmelCase						) and k[0] not in first_solution:
            A__       =     k[1]
            A__       =     k[0]

      first_solution.append(UpperCAmelCase						)
      A__       =     distance_of_first_solution + int(UpperCAmelCase						)
      A__       =     best_node

   first_solution.append(UpperCAmelCase						)

   A__       =     0
   for k in dict_of_neighbours[first_solution[-2]]:
      if k[0] == start_node:
         break
      position += 1

   A__       =     (
       distance_of_first_solution
       + int(dict_of_neighbours[first_solution[-2]][position][1]						)
       - 10000
   )
   return first_solution, distance_of_first_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for n in solution[1:-1]:
      A__       =     solution.index(UpperCAmelCase						)
      for kn in solution[1:-1]:
         A__       =     solution.index(UpperCAmelCase						)
         if n == kn:
            continue

         A__       =     copy.deepcopy(UpperCAmelCase						)
         A__       =     kn
         A__       =     n

         A__       =     0

         for k in _tmp[:-1]:
            A__       =     _tmp[_tmp.index(UpperCAmelCase						) + 1]
            for i in dict_of_neighbours[k]:
               if i[0] == next_node:
                  A__       =     distance + int(i[1]						)
         _tmp.append(UpperCAmelCase						)

         if _tmp not in neighborhood_of_solution:
            neighborhood_of_solution.append(_tmp						)

   A__       =     len(neighborhood_of_solution[0]						) - 1

   neighborhood_of_solution.sort(key=lambda UpperCAmelCase						: x[index_of_last_item_in_the_list]						)
   return neighborhood_of_solution







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     1
   A__       =     first_solution
   A__       =     []
   A__       =     distance_of_first_solution
   A__       =     solution

   while count <= iters:
      A__       =     find_neighborhood(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     0
      A__       =     neighborhood[index_of_best_solution]
      A__       =     len(UpperCAmelCase						) - 1

      A__       =     False
      while not found:
         A__       =     0
         while i < len(UpperCAmelCase						):
            if best_solution[i] != solution[i]:
               A__       =     best_solution[i]
               A__       =     solution[i]
               break
            A__       =     i + 1

         if [first_exchange_node, second_exchange_node] not in tabu_list and [
             second_exchange_node,
             first_exchange_node,
         ] not in tabu_list:
            tabu_list.append([first_exchange_node, second_exchange_node]						)
            A__       =     True
            A__       =     best_solution[:-1]
            A__       =     neighborhood[index_of_best_solution][best_cost_index]
            if cost < best_cost:
               A__       =     cost
               A__       =     solution
         else:
            A__       =     index_of_best_solution + 1
            A__       =     neighborhood[index_of_best_solution]

      if len(UpperCAmelCase						) >= size:
         tabu_list.pop(0						)

      A__       =     count + 1

   return best_solution_ever, best_cost







def 						_A      ( UpperCAmelCase=None						):






   '''simple docstring'''







   A__       =     generate_neighbours(args.File						)

   A__  ,			A__       =     generate_first_solution(
       args.File      ,UpperCAmelCase						)

   A__  ,			A__       =     tabu_search(
       UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,args.Iterations      ,args.Size      ,)

   print(F"""Best solution: {best_sol}, with total distance: {best_cost}."""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''Tabu Search''')
    parser.add_argument(
        '''-f''',
        '''--File''',
        type=str,
        help='''Path to the file containing the data''',
        required=True,
    )
    parser.add_argument(
        '''-i''',
        '''--Iterations''',
        type=int,
        help='''How many iterations the algorithm should perform''',
        required=True,
    )
    parser.add_argument(
        '''-s''', '''--Size''', type=int, help='''Size of the tabu list''', required=True
    )

    # Pass the arguments to main method
    main(parser.parse_args())


